It's 3 a.m., and you and a few other officers are on surveillance. The city has been plagued by a rash of hot-prowl burglaries over the past few weeks. The suspect has not been seen, usually because the victims were asleep when the burglary occurred.
The Crime Analysis Unit predicted the perp might strike in this area between the hours of midnight and 4 a.m. They also suggested he might be on foot or bicycle. His modus operandi is to remove louvered window panels or enter via unlocked doors, so he travels light and carries few tools. He likes to strike first-floor structures.
At 3:25 a.m., the radio silence is broken. "I see movement at post No. 3," one of the officers says. "One L31, the suspect just entered via a louvered window."
At 3:30 a.m., one of the officers reports: "One L32, code four, suspect in custody."
Any law enforcement agency could be the problem solver in this scenario by using predictive analytics to solve crimes before they occur again. In this instance, the local police crime analysis unit was able to process police report data from past incidents to predict when and where the next crimes were likely to occur. The watch commander only had to strengthen police staffing in targeted areas, and wait.
Advanced predictive analytics could be the next generation problem-solving tool for the policing profession. Predictive analytics combines existing technologies like computers, crime analysis and well-developed police reporting techniques and adds a few newer technologies such as artificial intelligence, universally shared data and borrowed technology from the consumer industry to build a system that is capable of predicting crime before it happens. The result is a network that gathers disparate data and uses software that is engineered and tested in the private customer management industry to model patterns and trends from crime data.
But high-tech tools alone cannot solve crimes. Crime analysts within the agency must analyze the results and offer police management insight as to where police resources are likely needed the most. And managers must balance their daily needs for patrol services with the potential to catch criminals through the strategic placement of officers in the field.
Predictive crime analysis packs potential
Law enforcement agencies have contributed crime data to the FBI's Uniform Crime Reporting (UCR) system since the 1930s. Participation in this system is not mandatory. However, as of 1995 the statistics represented in the UCR program included 95 percent of the nation's total population. The ability to quantify crime incidents in this way is one of the reasons predictive analytics presents such an excellent option for problem solving.
Consider that: According to the FBI Crime Clock (2006), residential burglaries occur once every 14.4 seconds in the United States. Of the roughly 2,183,746 residential burglaries reported nationwide, little more than 275,000 were cleared by arrest. These numbers alone seem to present a huge opportunity for policing to get ahead of the curve, and actually make a dent in reducing or eliminating property crimes altogether. The FBI Crime Clock also reported crimes of passion such as homicides occurred much less frequently; at an average of one every 30.9 minutes. In either case, it is possible to predict certain drug-related or domestic violence-related homicides because of the meticulous counting law enforcement does, reports Colleen McCue, author of "Doing More with Less — Data Mining in Police Deployment Decisions."
Leveraging computer tech
Fueled by the confluence of evolving computer technology, improved sharing of data between agencies and adaptations from the private sector, it is possible to bring reliable predictability and advanced problem solving to local police agencies. It's a matter of simple averages that computer technology alone will solve some crimes, and so will improved data sharing between law enforcement agencies. What modern policing needs to understand is that advanced predictive analytics acts as a force multiplier when all of these technologies are focused on problem solving.